Within recent years, data warehouse systems and business intelligence (BI) tools have established importance for supporting strategic business decisions. In a typical data warehouse system, a large amount of data is assembled periodically in a warehouse server, and then transformed or “cleansed” before it is generated and stored in a special data structure called a multidimensional database. These data structures can be stored on the same or another warehouse server that will be used for multi-dimensional analysis and reporting.
Inherent in conventional data warehouse architectures, and also one of the biggest challenges to effective BI processing, is a substantial time lag between when data is being generated and when data is available for reporting. Thus, as BI tools and data warehouse systems are increasingly employed for business strategy, there is ever more effort, albeit unsuccessful as yet, toward diminishing the data access latency to as close to zero as possible. Ideally, data would be reported immediately as it is being generated.